lightonai/nv-embed-supervised-distill-dedup-code

This dataset is a collection of the CoIR training datasets. We mined 2048 negatives per queries using gte-modernbert-base in order and format the data in a query, documents, scores format so that anyone can perform nv-retriever type of filtering using their own threshold (and this is also the format knowledge distillation for PyLate). Notably, this dataset has been used to perform the fine-tuning of the state-of-the-art late interaction LateOn-Code models. The boilerplate used to fine-tune… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/nv-embed-supervi

<iframe src="https://silytics.si/embed/item/31b4ceaf-95cf-4339-bd50-59f672660d7e" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.